Biology subjects

Rzhetsky, A.

Publications and source records attributed to Rzhetsky, A..

4 recordsLinked to original sources

bioRxiv2018

Pancreatic adenocarcinoma human organoids share structural and genetic features with primary tumors

Patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid systems show great promise for understanding the biological underpinnings of disease and advancing therapeutic precision medicine. Despite the increased use of organoids, the fidelity of molecular features, genetic heterogeneity, and drug response to the tumor of origin remain important unanswered questions limiting their utility. To address this gap in knowledge, we created primary tumor- and PDX-derived organoids, and 2D cultures for in-depth genomic and histopathological comparisons to the primary tumor. Histopathological features and PDAC representative protein markers showed strong concordance. DNA and RNA sequencing of single organoids revealed patient-specific genomic and transcriptomic consistency. Single-cell RNAseq demonstrated that organoids are primarily a clonal population. In drug response assays, organoids displayed patient-specific sensitivities. Additionally, we examined the in vivo PDX response to FOLFIRINOX and Gemcitabine/Abraxane treatments, which was recapitulated in vitro by organoids. The patient-specific molecular and histopathological fidelity of organoids indicate that they can be used to understand the etiology of the patients tumor and the differential response to therapies and suggests utility for predicting drug responses.

cancer biology↗

bioRxiv2017

Conjunction of Factors Triggering Waves of Seasonal Influenza

Understanding the subtle confluence of factors triggering pan-continental, seasonal epidemics of influenza-like illness is an extremely important problem, with the potential to save tens of thousands of lives and billions of dollars every year in the US alone. Beginning with several large, longitudinal datasets on putative factors and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of epidemics. Our analysis included insurance claims for a significant cross-section of the US population in the past decade, human movement patterns inferred from billions of tweets, whole-US weekly weather data covering the same time span as the medical records, data on vaccination coverage over the same period, and sequence variations of key viral proteins. We also explicitly accounted for the spatio-temporal auto-correlations of infectious waves, and a host of socioeconomic and demographic factors. We carried out multiple orthogonal statistical analyses on these diverse, large geo-temporal datasets to bolster and corroborate our findings. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions, the strongest predictor groups are as follows, ranked by importance: (1) the host populations socio- and ethno-demographic properties; (2) weather variables pertaining to relevant area specific humidity, temperature, and solar radiation; (3) the virus antigenic drift over time; (4) the host populations land-based travel habits, and; (5) the spatio-temporal dynamics immediate history, as reflected in the influenza wave autocorrelation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve {approx} 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

epidemiology↗

bioRxiv2017

Patchwork of contrasting medication cultures across the USA

Health care in the United States is markedly heterogeneous, with large disparities in treatment choices and health spending. Drug prescription is one major component of health care--reflecting the accuracy of diagnosis, the adherence to evidence-based guidelines, susceptibility to drug marketing, and regulatory factors. Using medical claims data covering nearly half of the USA population, we have developed a framework to compare prescription rates of 600 popular drugs in 2,334 counties. Our approach uncovers geographically separated sub-Americas, where patients receive treatment for different diseases, and where physicians choose different drugs for the same disease. The geographical variation suggests influences of racial composition, state-level health care laws, and wealth. Some regions consistently prefer more expensive drugs, even when they have not been proven more efficacious than cheaper alternatives. Our study underlines the benefit of aggregating massive information on medical practice into a summarized and actionable form. We hope that our methodology and conclusions will guide policy measures for aligning prescriptions with best-practice guidelines.

bioinformatics↗

bioRxiv2017

Universal Microfluidic System for Analysis and Control of Cell Dynamics

Dynamical control of the cellular microenvironment is highly desired for quantitative studies of stem cells and immune signaling. Here, we present an automated microfluidic system for high-throughput culture, differentiation and analysis of a wide range of cells in precisely defined dynamic microenvironments recapitulating cellular niches. This system delivers complex, time-varying biochemical signals to 1,500 independently programmable cultures containing either single cells, 2-D populations, or 3-D organoids, and dynamically stimulates adherent or non-adherent cells while tracking and retrieving them for end-point analysis. Using this system, we investigated the signaling landscape of neural stem cell differentiation under combinatorial and dynamic stimulation with growth factors. Experimental and computational analyses identified \"cellular logic rules\" for stem cell differentiation, and demonstrated the importance of signaling sequence and timing in brain development. This universal platform greatly enhances capabilities of microfluidic cell culture, allows dissection of previously hidden aspects of cellular dynamics, and enables accelerated biological discovery.

bioengineering↗